Machine learning, a part of AI, is rapidly integrated into financial companies and in today’s technologies have accelerated with Machine learning which analyses historical data, behaviors to predict patterns and make decisions.
Functions such as Key Analytical data sets Designed, development are now automated for banks/financial institutions. To leverage machine learning and predictive analytics to offer their customers a much more personalised experience, recommend new products and provide loyalty points or offers. or types of reports, of each different verticals to predict the data of merchants, customers, and behaviour patterns and needs of the items which help in forecasting the business of respective of our customers.
Historical and structured data in the financial services, making use of it as a perfect playing field for machine learning technologies.
Databases: Snowflake, BigData, Oracle, MS SQL Server
Language: Custom Python, Java, Power BI, SSIS, SSRS
ETL Tools: Tableau, Talend, Oracle Based ETL process
Regression & classification algorithms: Logistic Regression, Decision Trees, Random Forest, Dimension Reduction, K-Fold Cross validation, XGBoost, Handling Imbalanced dataset, Hyperparameter tuning Cross Validation, Gradient Boosting Machine(GBM) Algorithm, Ada Boost Classifier Algorithm
Neural network & computer vision: Tensor flow, Keras & openCV
Build tools: Oracle Data modeller, SQL Developer, Linux Shell Scripting, AWS S3 Buckets, SnowSQL, DBeaver
We practise delivery methodology with a blend of onsite and offshore resources exercising a full-bodied communication grid. Customers can successfully accomplish their IT budget and balance their project priorities by indicating the crucial services whenever needed, and for as long as essential.
Our Database Services guarantees that customer data and databases are safeguarded and supervised by establishing High-Availability, backup and recovery measures, delivering secured database environments, and examining database performance as well. Database software support is available for all popular databases and releases.
Technology stack selection
Requirement analysis & design of databases
Initial database software installation in various environments
Data conversion/migration execution
Database security maintenance
Configuration & verification as required
Database High-Availability (business continuity), monitoring & management
Performance tuning & monitoring
Designing, executing Backup & Restore policies
Systematic treatment of Data at rest & Obsolete
Administration & monitoring
Database restoration, as required
Administration of various DBA functions
Respond, resolve issues related to alerts & customer requirements
Machine learning functions such as fraud detection and credit scoring and Payment Devices are some part which general banks/financial institutions leverages to their customers, but the financial services tend to encounter enormous volumes of data relating to daily transactions, bills, payments, vendors, and customers, which are perfect for machine learning.
In recent days, many leading financial services companies are incorporating machine learning into their operations, resulting in a better-streamlined process, reduced risks, and better-optimize.
Build a best fit model, based on previous payment history Loan delinquency prediction using machine learning algorithms using Python, Statistics, Logistic Regression, Cross Validation, Gradient Boosting Machine (GBM) Algorithm, Random Forest, Ada Boost Classifier Algorithm
We engage with our customers for DB related services as per their needs and at the stage they need our services for short and long term.
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